In silico Tools at Early Stage of Pharmaceutical Development: Data Needs and Software Capabilities

Development of a Discriminative Dissolution Method, Using In-Silico Tool for Hydrochlorothiazide and Valsartan Tablets

Hydrochlorothiazide (HTZ) and Valsartan (VAL) are poorly soluble drugs in BCS classes IV and II. This study aimed to develop a method to assess the dissolution profile of tablets containing HTZ (12.5 mg) and VAL (160 mg) as a fixed-dose combination, using in silico tools to evaluate products marketed in Brazil and Peru. Firstly, in vitro dissolution tests were performed using a fractional factorial design 3^3-1. Then, DDDPlus™ was used to carry out experimental design assays of a complete factorial design 3³. Data from the first stage were used to obtain calibration constants for in silico simulations. The factors used in both designs were formulation, sinker use, and rotation speed. Finally, effects and factor interaction assessment was evaluated based on a statistical analysis of the dissolution efficiency (DE) obtained from simulations. Thus, the established final conditions of the dissolution method were 900 mL of phosphate buffer pH 6.8, 75 rpm of rotation speed, and sinker use to prevent formulation floating. The reference product stood out because of its higher DE than other formulations. It was concluded that the proposed method, in addition to ensuring total HTZ and VAL release from formulations, has adequate discriminative power.

An In Vitro-In Vivo Simulation Approach for the Prediction of Bioequivalence

The aim of this study was to develop a new in vitro–in vivo simulation (IVIVS) approach in order to predict the outcome of a bioequivalence study. The predictability of the IVIVS procedure was evaluated through its application in the development process of a new generic product of amlodipine/irbesartan/hydrochlorothiazide. The developed IVIVS methodology is composed of three parts: (a) mathematical description of in vitro dissolution profiles, (b) mathematical description of in vivo kinetics, and (c) development of joint in vitro–in vivo simulations. The entire programming was done in MATLAB^® and all created scripts were validated through other software. The IVIVS approach can be implemented for any number of subjects, clinical design, variability and can be repeated for thousands of times using Monte Carlo techniques. The probability of success of each scenario is recorded and finally, an overall assessment is made in order to select the most suitable batch. Alternatively, if the IVIVS shows reduced probability of BE success, the R&D department is advised to reformulate the product. In this study, the IVIVS approach predicted successfully the BE outcome of the three drugs. During the development of generics, the IVIVS approach can save time and expenses.